[Help]: How to combine multiple indexes into one ?

[byronz3d]

Question Validation

• I have searched both the documentation and discord for an answer.

Question

Hi,

I want to create several collections in my Zep server, and then when a prompt/query arrives, I want to combine several collections into one index and be able to query the combined index.
Is this even possible?

What I tried to do, unsuccessfully, is something like:

def load_zep(collection_name):
        vector_store = ZepVectorStore(
            api_url=zep_api_url, collection_name=collection_name
        )

        return StorageContext.from_defaults(vector_store=vector_store,persist_dir=f'./storage/{collection_name}')

                from llama_index import load_index_from_storage, StorageContext

                #load different collections 
                storage_context1 = load_zep('collection1')
                storage_context2 = load_zep('collection2')
                storage_context3 = load_zep('collection3')

                #some how combine the above 3 collections into one

                #tried various ways like combined = list(), but nothing worked

                #load index from above combined 
                index = load_index_from_storage(combined) 

The logic behind this is I have various user levels and based on the user I want to give them a different index. Higher level users gets more indexes, while low level user gets a single index to query. I don't want to create the same index over and over if I can combine them somehow.

Thanks in advance!

[dosubot[bot]]

🤖

Hi,

Based on your question, it seems you want to merge multiple indices into a single one. You can achieve this using the GPTListIndex class in LlamaIndex. Here's a code snippet on how to do it:

from llama_index import GPTListIndex

index = GPTListIndex([index1, index2])

After merging the indices, you can build a graph and save it to disk:

from llama_index import ComposableGraph

all_indices = [index1, index2, index3, index4]
index_summaries=[index1_summary, index2_summary, index3_summary, index4_summary]

graph = ComposableGraph.from_indices(GPTTreeIndex, all_indices, index_summaries=index_summaries)    
graph.save_to_disk("graph.json")

To load the saved graph, use the following code:

index = ComposableGraph.load_from_disk("graph.json")

This solution was suggested in a similar issue: How can merge multi index file

Regarding your issue with ChromaDB, LlamaIndex handles the storage and retrieval of vectors in ChromaDB through the ChromaVectorStore class. The add method is used for storage and the query method is used for retrieval. If you're having trouble storing more than two files, it might be due to an issue with your ChromaDB setup or the data you're trying to store.

If you could provide more details about the error you're encountering (any error messages, the data you're trying to store, etc.), it would be easier to diagnose the problem.

I hope this helps! Let me know if you have any other questions.

Sources

• [Question]: Connectiing multiple index to create one chat engine
• How can merge multi index file
• llama_index/vector_stores/milvus.py
• llama_index/indices/list/base.py

---

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

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[byronz3d]

@dosu-bot
AttributeError: 'ComposableGraph' object has no attribute 'save_to_disk'

[dosubot[bot]]

🤖

Hello,

The error you're encountering is due to the fact that the ComposableGraph class in LlamaIndex does not have a save_to_disk method. This is why you're seeing the AttributeError: 'ComposableGraph' object has no attribute 'save_to_disk'.

There are a couple of ways to resolve this issue:

1. Implement a save_to_disk method in the ComposableGraph class: This method should handle the logic for saving the graph to disk. Here's a simple example using Python's built-in pickle module for serialization:

import pickle

class ComposableGraph:
    # existing code...

    def save_to_disk(self, file_path):
        """Save the graph to disk."""
        with open(file_path, 'wb') as f:
            pickle.dump(self, f)

In this example, the save_to_disk method takes a file_path argument and uses pickle.dump to serialize the ComposableGraph object and write it to the specified file. Please note that this is a simple example and might not suit your needs exactly. For instance, if your ComposableGraph objects are very large, you might need to use a more efficient serialization format. Or, if you want to save the graphs to a remote location, you would need to add code to handle that.

2. Use the StorageContext class to persist data to disk: The StorageContext class in LlamaIndex is used to persist data to disk, not the ComposableGraph class. The persist method of the StorageContext class is used to save the storage context to disk. The ComposableGraph is then loaded from this persisted storage context using the load_graph_from_storage function. Here's the relevant code:

# persist storage to disk
storage_context.persist(str(tmp_path))

# load storage context
new_storage_context = StorageContext.from_defaults(persist_dir=str(tmp_path))

# load index
new_graph = load_graph_from_storage(
    new_storage_context, root_id=graph.root_id, service_context=mock_service_context
)

You can find this code in the tests/indices/test_loading_graph.py file in the LlamaIndex repository.

I hope this helps! Let me know if you have any other questions.

Sources

• Is there any default way to save a composable graph index?
• llama_index/indices/composability/graph.py
• tests/indices/test_loading_graph.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[sumitsahoo]

@byronz3d You can persist the graph using below code

# set the ID
graph.root_index.set_index_id("my_id")

# persist to storage
graph.root_index.storage_context.persist(persist_dir="./storage")

# load
from llama_index import StorageContext, load_graph_from_storage

storage_context = StorageContext.from_defaults(persist_dir="./storage")
graph = load_graph_from_storage(storage_context, root_id="my_id")

You can find the code snippet from core module document: https://docs.llamaindex.ai/en/stable/core_modules/data_modules/index/composability.html#optional-persisting-the-graph

Yes, it should have been easier to find in the documentation. I myself found it today. Hope it helps.